Search Results for author: Peng Fu

Found 17 papers, 11 papers with code

Target Really Matters: Target-aware Contrastive Learning and Consistency Regularization for Few-shot Stance Detection

1 code implementation COLING 2022 Rui Liu, Zheng Lin, Huishan Ji, Jiangnan Li, Peng Fu, Weiping Wang

Despite the significant progress on this task, it is extremely time-consuming and budget-unfriendly to collect sufficient high-quality labeled data for every new target under fully-supervised learning, whereas unlabeled data can be collected easier.

Contrastive Learning Stance Detection

Question-Interlocutor Scope Realized Graph Modeling over Key Utterances for Dialogue Reading Comprehension

no code implementations26 Oct 2022 Jiangnan Li, Mo Yu, Fandong Meng, Zheng Lin, Peng Fu, Weiping Wang, Jie zhou

Although these tasks are effective, there are still urging problems: (1) randomly masking speakers regardless of the question cannot map the speaker mentioned in the question to the corresponding speaker in the dialogue, and ignores the speaker-centric nature of utterances.

Reading Comprehension

Compressing And Debiasing Vision-Language Pre-Trained Models for Visual Question Answering

no code implementations26 Oct 2022 Qingyi Si, Yuanxin Liu, Zheng Lin, Peng Fu, Weiping Wang

To facilitate the application of VLP to VQA tasks, it is imperative to jointly study VLP compression and OOD robustness, which, however, has not yet been explored.

Question Answering Visual Question Answering (VQA)

A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models

1 code implementation11 Oct 2022 Yuanxin Liu, Fandong Meng, Zheng Lin, Jiangnan Li, Peng Fu, Yanan Cao, Weiping Wang, Jie zhou

In response to the efficiency problem, recent studies show that dense PLMs can be replaced with sparse subnetworks without hurting the performance.

Natural Language Understanding

Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning

1 code implementation10 Oct 2022 Qingyi Si, Yuanxin Liu, Fandong Meng, Zheng Lin, Peng Fu, Yanan Cao, Weiping Wang, Jie zhou

However, these models reveal a trade-off that the improvements on OOD data severely sacrifice the performance on the in-distribution (ID) data (which is dominated by the biased samples).

Contrastive Learning Question Answering +1

Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA

1 code implementation10 Oct 2022 Qingyi Si, Fandong Meng, Mingyu Zheng, Zheng Lin, Yuanxin Liu, Peng Fu, Yanan Cao, Weiping Wang, Jie zhou

To overcome this limitation, we propose a new dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets.

Question Answering Visual Question Answering (VQA)

Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training

1 code implementation NAACL 2022 Yuanxin Liu, Fandong Meng, Zheng Lin, Peng Fu, Yanan Cao, Weiping Wang, Jie zhou

Firstly, we discover that the success of magnitude pruning can be attributed to the preserved pre-training performance, which correlates with the downstream transferability.

Transfer Learning

Check It Again: Progressive Visual Question Answering via Visual Entailment

1 code implementation8 Jun 2021 Qingyi Si, Zheng Lin, Mingyu Zheng, Peng Fu, Weiping Wang

Besides, they only explore the interaction between image and question, ignoring the semantics of candidate answers.

Question Answering Visual Entailment +1

A Hierarchical Transformer with Speaker Modeling for Emotion Recognition in Conversation

1 code implementation29 Dec 2020 Jiangnan Li, Zheng Lin, Peng Fu, Qingyi Si, Weiping Wang

It can be regarded as a personalized and interactive emotion recognition task, which is supposed to consider not only the semantic information of text but also the influences from speakers.

Emotion Recognition in Conversation

Learning Class-Transductive Intent Representations for Zero-shot Intent Detection

1 code implementation3 Dec 2020 Qingyi Si, Yuanxin Liu, Peng Fu, Zheng Lin, Jiangnan Li, Weiping Wang

A critical problem behind these limitations is that the representations of unseen intents cannot be learned in the training stage.

Intent Detection Multi-Task Learning +1

Modeling Intra and Inter-modality Incongruity for Multi-Modal Sarcasm Detection

no code implementations Findings of the Association for Computational Linguistics 2020 Hongliang Pan, Zheng Lin, Peng Fu, Yatao Qi, Weiping Wang

Inspired by this, we propose a BERT architecture-based model, which concentrates on both intra and inter-modality incongruity for multi-modal sarcasm detection.

Sarcasm Detection

Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion

no code implementations18 Sep 2020 Zixian Ge, Guo Cao, Xuesong Li, Peng Fu

Then, by means of the multibranch neural network, three kinds of features from shallow to deep are extracted and fused in the spectral dimension.

Classification General Classification +1

Cannot find the paper you are looking for? You can Submit a new open access paper.